A double-stage genetic optimization algorithm for portfolio selection

Authors:
Kin Keung Lai;Lean Yu;Shouyang Wang;Chengxiong Zhou
Affiliations:
College of Business Administration, Hunan University, Changsha, China and Department of Management Sciences, City University of Hong Kong, Kowloon, Hong Kong;Department of Management Sciences, City University of Hong Kong, Kowloon, Hong Kong and Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beiji ...;College of Business Administration, Hunan University, Changsha, China and Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China;Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Year:
2006

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Abstract

In this study, a double-stage genetic optimization algorithm is proposed for portfolio selection. In the first stage, a genetic algorithm is used to identify good quality assets in terms of asset ranking. In the second stage, investment allocation in the selected good quality assets is optimized using a genetic algorithm based on Markowitz's theory. Through the two-stage genetic optimization process, an optimal portfolio can be determined. Experimental results reveal that the proposed double-stage genetic optimization algorithm for portfolio selection provides a very feasible and useful tool to assist the investors in planning their investment strategy and constructing their portfolio.